Both n8n and Make can automate AI workflows, but they approach the problem differently. After building production automations with both platforms, here’s what actually matters for AI-specific use cases.

Core Philosophy Differences

n8n: Open source, self-hostable, developer-friendly. Built for technical users who want control and customization.

Make: Cloud-native, visual-first, business-user friendly. Built for rapid prototyping with less technical overhead.

This philosophical difference shapes everything from pricing to capabilities.

Quick Comparison Table

Feature	n8n	Make
Pricing model	Execution-based (cloud) or self-host free	Operation-based
Self-hosting	Yes, Docker/Kubernetes	No
AI integrations	Native + custom code	Native prebuilt
Custom code	Full JavaScript/Python	Limited
Learning curve	Steeper	Gentler
Error handling	Robust, developer-focused	Visual, simpler
Version control	Git integration available	Limited
Community	Open source, active	Proprietary, templates

AI Integration Capabilities

n8n AI Features

Native AI nodes:

• OpenAI (GPT models)
• Anthropic (Claude)
• Google AI (Gemini)
• Hugging Face
• Replicate
• Vector databases (Pinecone, Qdrant)

Custom integration: Full JavaScript/Python support means any LLM API is accessible. Call local Ollama, custom endpoints, or proprietary services with HTTP requests + code.

Langchain integration: n8n has built-in Langchain components for:

• Document loaders
• Text splitters
• Vector stores
• Retrieval chains

Make AI Features

Native modules:

• OpenAI
• Anthropic
• Google AI
• Cohere
• HTTP modules for anything else

Limitations: Less flexibility for custom code. Complex AI chains require more workarounds.

For more on n8n AI capabilities, see the n8n for AI automation tutorial.

Pricing Comparison

n8n Pricing

Self-hosted (free):

• Unlimited executions
• Your infrastructure costs only
• Full feature access

n8n Cloud:

• Free: 2,500 executions/month
• Starter: $20/month for 2,500 executions
• Pro: $50/month for 10,000 executions
• Enterprise: Custom pricing

Key insight: Self-hosted n8n is free. For high-volume AI workflows, this changes the math entirely.

Make Pricing

Cloud-only:

• Free: 1,000 operations/month
• Core: $9/month for 10,000 operations
• Pro: $16/month for 10,000 operations
• Teams: $29/month for 10,000 operations

Key insight: “Operations” count differently than “executions.” A single workflow might use 5-20 operations, making Make more expensive at scale.

Real Cost Scenario

AI content workflow (100 runs/day):

n8n self-hosted: Infrastructure only (~$10-50/month VPS) n8n cloud: ~$150/month (Pro tier) Make: ~$50-100/month depending on operation count

For AI workflows specifically, n8n self-hosting is dramatically cheaper at scale.

Self-Hosting Considerations

n8n Self-Hosting

Advantages:

• Zero platform fees
• Data never leaves your infrastructure
• Full control over resources
• No vendor lock-in

What you need:

• Docker-capable server
• Basic DevOps knowledge
• Backup strategy
• Update process

Deployment: Simple Docker Compose setup works for most use cases. Kubernetes for scale.

The Docker for AI engineers guide covers containerization fundamentals.

Make: No Self-Hosting

Make is cloud-only. If you need data sovereignty or self-hosting, it’s not an option.

Workflow Complexity Handling

Simple AI Workflow (Summarize emails)

Both handle well:

1. Trigger on new email
2. Extract content
3. Send to LLM for summary
4. Save/send summary

Winner: Make - Faster to set up with visual interface.

Medium AI Workflow (RAG chatbot)

Steps:

1. Receive webhook
2. Embed query
3. Search vector database
4. Retrieve relevant documents
5. Construct prompt with context
6. Send to LLM
7. Return response

Winner: n8n - Better handling of complex data flows and custom code for prompt construction.

Complex AI Workflow (Multi-model orchestration)

Steps:

1. Classify incoming request
2. Route to appropriate model
3. Handle errors and retries
4. Combine results from multiple models
5. Post-process with validation
6. Log everything for debugging

Winner: n8n clearly - Custom code, robust error handling, and self-hosting enable production-grade complexity.

The n8n vs Python for AI automation explores when to go fully custom.

Error Handling and Debugging

n8n Error Handling

Features:

• Error workflows (separate flow on failure)
• Retry logic per node
• Detailed execution logs
• Manual re-runs from specific points

For AI workflows: LLM calls fail sometimes. n8n’s retry logic and error branching handle this gracefully.

Make Error Handling

Features:

• Error handlers per scenario
• Basic retry options
• Execution history

For AI workflows: Adequate for simple flows. Complex error recovery requires workarounds.

Version Control and Collaboration

n8n

• Export workflows as JSON
• Git integration possible
• Self-hosted enables any collaboration model

Make

• Limited version history
• Blueprint import/export
• Teams tier required for collaboration

For production AI workflows, version control matters. n8n’s export capability fits engineering workflows better.

Performance at Scale

n8n Performance

Self-hosted scaling:

• Horizontal scaling with workers
• Queue-based execution
• Handle thousands of concurrent executions

Bottleneck: Your infrastructure, not the platform.

Make Performance

Cloud scaling:

• Platform manages scale
• Rate limits apply
• Operations counted against quota

Bottleneck: Pricing tier limits throughput.

Decision Framework

Choose n8n When

1. Self-hosting required - Data sovereignty, compliance, or cost
2. High volume - 10K+ executions monthly
3. Complex AI chains - Multi-step, multi-model workflows
4. Technical team - Comfortable with some code and DevOps
5. Customization needed - Non-standard integrations or logic

Choose Make When

1. Speed to prototype - Need something working in hours
2. Non-technical users - Business users building automations
3. Simple AI workflows - Single LLM call, straightforward logic
4. No DevOps capacity - Can’t manage infrastructure
5. Visual preference - Drag-and-drop is important

Consider Zapier Instead When

• Simplest possible workflows
• Widest app integration library needed
• AI is minor component of automation

The n8n vs Zapier comparison covers this specific decision.

Migration Between Platforms

Make to n8n

Process:

1. Document Make scenarios
2. Rebuild in n8n (no direct import)
3. Test thoroughly
4. Migrate webhook URLs

Difficulty: Medium - concepts translate but workflows don’t import directly.

n8n to Make

Process:

1. Export n8n workflows
2. Manually recreate in Make
3. Simplify complex code nodes (Make has limited code support)

Difficulty: High - n8n workflows often have complexity Make can’t match.

Production Recommendations

For AI engineering teams:

n8n self-hosted is the clear winner. The combination of:

• Zero per-execution fees
• Full code access for AI-specific logic
• Data staying on your infrastructure
• Version control integration

Makes it the better choice for serious AI automation.

For business teams with AI needs:

Make gets you started faster. When workflows outgrow it, migrate to n8n or custom code.

For prototyping:

Make’s visual interface enables faster iteration. Build proof-of-concept in Make, production in n8n or Python.

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Building AI automations?

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